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Introduction

Ironman Triathlons over 3.8-km swimming, 180-km cycling, and 42.2-km running are increasing in popularity. Every year, more and more athletes participate in these races to qualify for the Ironman World Championship in Hawaii (20). Finishing such an ultraendurance race needs an enormous physical effort.

The question is which kind of body anthropometry may sustain this type of performance in the fastest possible time. For short-distance triathletes, Landers et al. (14) found that low levels of adiposity were important for total race time and most of the subdisciplines. In addition, longer segmental lengths were important for a successful swimming outcome. According to Sleivert and Rowlands (24), male elite triathletes were generally tall, of average to light weight, and had low levels of body fat. In contrast to these anthropometric findings, Leake and Carter (16) concluded that training parameters such as training distance, training time, and training experience (years) were more important than anthropometric measures in the prediction of performance for female triathletes in short-distance races. According to O'Toole (22), training distances appear to be more important than training paces in preparation for an ultraendurance triathlon.

There are studies in short-distance triathlons (14,15,24) and ultradistance triathlons over distances longer than the Ironman Triathlon (12,13) about the relationship of anthropometry to performance. However, there are little data about the association of anthropometry and training on race performance in an Ironman Triathlon (22). The aim of this present investigation was, therefore, to investigate in recreational male and female Ironman triathletes whether we could find an association between anthropometric and training variables with race performance in an Ironman.

Methods

Experimental Approach to the Problem

The organizer of the Ironman Switzerland in 2007 contacted all participants via a separate newsletter 3 months before the race and asked them to participate in our investigation. On the June 24, 2007, Ironman Switzerland took place in the heart of the City of Zurich, Switzerland. One thousand nine hundred and fifty male and female Ironman triathletes from 49 countries started in the morning at 07:00 am The air temperature was 17°C, and that of the water in Lake Zurich was 20°C. Because of the low temperature, wet suits were allowed. Relative humidity was 88% at the start of the race, 43% at noon, and 41% in the evening. At the start, the sky was blue and became cloudy slowly during the afternoon and evening. The highest temperature, 25°C, was reached in the afternoon. The athletes had to swim 2 laps in the lake to cover the 3.8-km distance and then had to cycle 3 laps of 60 km each, which was followed by running 4 laps of 10.5 km each. In the cycling section, the highest point to climb from Zurich (400 m·asl) was the “Forch” (700 m·asl), although the running course was completely flat in the City of Zurich. Nutrition was provided during the cycling and running courses by the organizers. They offered bananas, energy bars, energy gels, carbohydrate drinks, caffeinated drinks, and water on the cycling course. On the running course, in addition to the aforementioned nutrition, different fresh fruits, dried fruits, nuts, chips, salt bars, and soup were provided.

Subjects

Forty male and 20 female Caucasian nonprofessional Ironman triathletes were interested in our study; 30 male and 18 female athletes entered the investigation. The study was approved by the Institutional Review Board for use of Human subjects of the Canton of St. Gallen, Switzerland. The athletes were informed of the experimental risks and gave their informed written consent. Twenty-seven male and 16 female athletes of our study group (Table 1) finished the race successfully within the time limits. Three male and 2 female triathletes had to give up because of medical complications. Training parameters for our successful male and female finishers are shown in Table 2. Twenty-two male athletes had already finished 4.4 (5.5) Ironman races (range 1-28 races), and 11 female athletes had finished 1.6 (0.6) races (range 1-2 races).

Procedures

Before the start of the race, every participant underwent anthropometric measurements to determine body mass, body height, and skin-fold thicknesses to calculate body mass index (BMI), sum of 8 skin folds, percent body fat, and skeletal muscle mass using the anthropometric method. Body height was measured with a stadiometer to the nearest 1.0 cm. Body mass was measured with a commercial scale (Beurer BF 15, Beurer, Ulm, Germany) to the nearest 0.1 kg. Circumferences of the upper arm, thigh, and calf were measured at the largest circumference of the limb to the nearest 0.1 cm. At the thigh, circumference was determined 20 cm above the upper pole of the patella. Circumference of the hip for females was measured at the level of the trochanter major to the nearest 0.1 cm. Skin-fold thicknesses of chest, midaxillary (vertical), triceps, subscapular, abdominal (vertical), suprailiac (at anterior axillary), thigh, and calf were measured with a skin-fold calliper (GPM-Hautfaltenmessgerät, Siber & Hegner, Zurich, Switzerland) to the nearest 0.2 mm at the right side of the body, to calculate percent body fat using the anthropometric method (4). The skin-fold measurements were taken once throughout the entire 8 skin folds and was then repeated twice more by the same investigator; the mean of the 3 times was then used for the analyses. The timing of the taking of the skin-fold measurements was standardized to ensure reliability. According to Becque et al. (6), readings were taken 4 seconds after applying the calliper. One trained investigator took all measurements, because intertester variability is a major source of error in skin-fold measurements. An intratester reliability check was conducted on 27 male runners before testing. No significant difference between the 2 trials for the sum of 8 skin folds was observed (p > 0.05). The intraclass correlation was high at r = 0.99. The same investigator was also compared with another trained investigator to determine objectivity. No significant difference existed between testers (r = 0.97; p > 0.05). For male athletes, percent body fat was calculated using the following anthropometric formula for men: Percent body fat = 0.465 + 0.180(∑7SF) − 0.0002406(∑7SF)2 + 0.0661(age), where ∑7SF = sum of skin-fold thickness of chest, midaxillary, triceps, subscapular, abdomen, suprailiac, and thigh mean, according to Ball et al. (4). This formula was evaluated on 160 men aged 18-62 years old and crossvalidated using dual-energy X-ray absorptiometry (DXA). The mean differences between DXA percent body fat and calculated percent body fat ranged from 3.0 to 3.2%. Significant (p < 0.01) and high (r > 0.90) correlations existed between the anthropometric prediction equations and DXA. For female athletes, percent body fat was calculated using the following anthropometric formula for women: Percent body fat = − 6.40665 + 0.41946(∑3SF) −0.00126(∑3SF)2 + 0.12515(hip) + 0.06473(age), according to Ball et al. (5). Skeletal muscle mass was calculated using the following anthropometric formula: Skeletal muscle mass = Ht(0.00744CAG2 + 0.00088CTG2 + 0.00441CCG2) + 2.4 sex − 0.048 age + race + 7.8, where Ht = height, CAG = skin fold-corrected upper arm girth, CTG = skin fold-corrected thigh girth, CCG = skin fold-corrected calf girth, sex = 1 for men and 0 for women, race = 0 for white, according to Lee et al. (18). This anthropometric method was evaluated on 189 nonobese subjects and crossvalidated using magnetic resonance imagining evaluation. Three months before the race, the athletes were asked to record their average weekly endurance training volume in hours and kilometers per discipline, covering the last 3 months in preparation for the race. During these 3 months before the race, each athlete maintained a comprehensive training diary consisting of daily workouts with distance and duration per discipline, because training volume is crucial for endurance athletes (23). The athletes were not asked to record eventual resistance training. Furthermore, athletes were asked about the number of their finished Ironman races.

Statistical Analyses

All continuous data were tested for Gaussian distribution using the Shapiro-Wilk test, and this is presented as mean (SD) or median (interquartile range [IQR]) as appropriate. Unpaired t-test and the Wilcoxon rank-sum test were used for comparisons between genders. Gender-specific associations between race performance, anthropometry, and training volume were investigated by parametric- and nonparametric correlation analysis as appropriate. The independent association between race performance, anthropometric variables, and training volume was assessed by means of multiple linear regressions with total race time as dependent variable. Variables that showed a significant difference between genders were included as covariates in the regression model. To further investigate gender-specific differences on race performance, interaction terms between anthropometric and training variables with gender were included in the model. Interactions with a p value ≤ 0.05 were considered statistically significant. The soundness of the fitted models (i.e., r2 of the model with and without interaction terms) was compared by a Wald test. We performed residual analysis of the final model to check for violations of the regression assumptions (i.e., independence, Gaussian distribution, and homoscedasticity).

Results

Performance

The male athletes finished the race within 697 (74) minutes (CV = 10.5%). For the swim split, they invested 76 (16) minutes (CV = 21.0%), for the bike 357 (39) minutes (CV = 10.9%), and for the run 264 (38) minutes (CV = 14.4%). The females finished within 762 (88) minutes (CV = 11.5%). They completed the swim within 81 (13) minutes (CV = 16%), the bike in 390 (41) minutes (CV = 10.5%), and the run within 291 (41) minutes (CV = 14.1%). Out of 1,482 ranked male athletes, we have the results from 27 (1.2) finishers. Our best subject finished in the 48th place, our last subject in the 1,398th place. Out of these 27 subjects, 8 athletes (29.6%) finished in the first third, 13 racers (48.2%) in the middle third, and 6 athletes in the last third (22.2%). The ranking of the male subjects is representative for the whole field. Among the women, our best subject was in the 9th place, our last subject in the 155th place within the 161 ranked female racers. In contrast to the men, the women were nonnormally distributed. Five subjects (31.3) finished in the first third, 4 women (25%) in the middle third, and 7 women (43.8%) in the last third. Therefore, in the women, the subjects were more representative of the slower participants.

Training

Training volume in swimming (r2 = 0.02; p > 0.05), cycling (r2 = 0.04; p > 0.05), and running (r2 = 0.05; p > 0.05) were not associated with performance in the corresponding race splits. Speed in training in the subdisciplines was not related to speed split in the race (p > 0.05) for all subdisciplines. For the female triathletes, percent body fat was not associated with total race time (Figure 4), whereas training volume was related to total race time (Figure 5). Percent body fat was not associated with average weekly training volume (Figure 6) in women. For the subdisciplines, percent body fat showed no relationship with swim (r2 = 0.03; p > 0.05), bike (r2 = 0.06; p > 0.05), or run (r2 = 0.03; p > 0.05) split time in the race. Regarding training volume, the volume in swim training was related to split time in the race (r2 = 0.23; p < 0.05) but not the volume in cycling (r2 = 0.19; p > 0.05) and running (r2 = 0.16; p > 0.05). Speed in training in the subdisciplines was not related to speed in the splits during the race (p > 0.05) for all subdisciplines. Results of the multiple linear regression models investigating the independent association between race time and athletes' characteristics are presented in Table 3. Interaction terms between sex and training volume and sex and percent body fat reached statistical significance (Table 3, model 1). The soundness of the fitted model (r2) including interaction terms was significantly better compared with the model, not taking into account potential interactions (62% vs. 47%) (p = 0.002 for model comparison). Percent body fat was significantly and positively associated with race time in male athletes (Table 3, model 2), whereas training volume was independently and negatively associated with race time in female athletes (Table 3, model 3) when controlled for BMI, body mass, and skeletal muscle mass. Results of the residual analysis for the various models fulfilled the regression assumptions.

Discussion

The main finding of this investigation was that anthropometry and training volume were differentially correlated to total race time in both male and female Ironman triathletes, and that percent body fat was not associated with training volume for both genders. This cross-sectional study is limited regarding the influence and effects of anthropometry and both volume and intensity in training on race performance in Ironman triathletes, because only an intervention trial can answer this question. Other limitations are lack of fitness evaluation and experience of these athletes. The crude mean (SE) difference in race performance between the sexes was 65 (25) minutes, corresponding to a large effect size of 0.82 (Cohen's d). Effect size calculations for the bivariate associations between race performance and body fat in men and race performance and training volume in women were both >0.8 and have to be considered as large.

It seems from our results that male Ironman triathletes are very similar to runners from an anthropometric point of view. Our Ironman triathletes seemed to profit from a low percent body fat, which seemed also to enable a faster total race time (Figure 1). A relationship of this anthropometric variable with performance has already been found in runners over middle distances up to the marathon. Regional and total body fat correlated inversely with performance in an incremental treadmill exercise test in runners (10). Mattila et al. (21) found that fat percentage is significantly associated with 12-minute running performance. Elite runners over 10 km (2) and the marathon distance (3) also had low skin-fold values and low body fat. In addition, low body mass was advantageous in ultrarunners (11), and a low BMI was associated with race performance in female marathon runners (8). However, in our male and female triathletes, neither body mass nor BMI was correlated with total race time. The reason must be the higher BMI, because BMI in our triathletes was higher when compared with that of the male (11) and female (3) runners.

There are several studies showing an association of skin-fold thicknesses with performance in running. The finding that the total sum of skin folds is associated with performance was described by Bale et al. (2) in runners over 10 km. Arrese and Ostariz (1) could show high correlations between the front thigh and medial calf skin folds and the 1,500-m run time, and between the front thigh and medial calf skin fold and the 10,000-m run time. However, the total sum of 6 skin folds was not associated with performance for any of the distances. It seems that with increasing length of a running event, the skin-fold thicknesses become more important. In marathon runners compared with runners over shorter distances, all skin-fold thicknesses are significantly lower (19). Intense training leads to a significant decrease of the sum of 6 skin folds in runners according to Legaz and Eston (18) thus leading to reduced body fat. However, in our triathletes, total training volume in hours per week showed no association with percent body fat for neither our male (Figure 3) nor female Ironman triathletes (Figure 6). Percent body fat was calculated in both women (3) and men (4) using the anthropometric method based upon determination of skin-fold thicknesses. Although we found a relationship between percent body fat and race time in male athletes (Figure 1), no association was found for females (r2 = 0.00, p > 0.05). Recent studies with runners suggest that skin-fold thicknesses are related to training volume and competitive athletes have lower skin-fold thicknesses because of higher training volume (18,19). We would therefore expect that training volume and percent body fat would show a relationship. However, for both male (Figure 3) and females athletes (Figure 6), we found no association between average weekly training volume and percent body fat. Legaz Arrese et al. (19) concluded from their study of male and female top class runners over different distances that body fat was only related to marathon performance, which was probably because of higher training volume. Ironman triathletes have to run a marathon at the end of the triathlon. Obviously, in these athletes, training shows no association with body fat, and we might presume that training seems to have no influence on body fat. Because Legaz and Eston (18) investigated in runners the relationship between training and skin-fold thickness, we checked for a relationship between percent body fat and speed in training during running. Both in men (r = 0.22, p > 0.05) and in women (r = −0.41, p > 0.05), intensity during running showed no relationship with percent body fat.

A further question is whether training volume might be of importance regarding race performance in Ironman triathletes. The training of our athletes is summarized in Table 2. We found no association of training volume with total race time for the male athletes (Figure 2). This study has examined a rather small sample of recreational male (n = 27) and female (n = 16) Ironman triathletes. Our male athletes finished the race within 11.25:22.0 (1.25:17.4) hours: minutes, the female athletes in 12.12:34.0 (1.5:19.2) hours: minutes. Elite male Ironman triathletes complete the distance in 8:30 to 9:00 hours: minutes, depending on the environment and the course. One might anticipate that a study of a larger cohort that included elite, and recreational, triathletes would show that training parameters do, in fact, influence race performance. The small sample size of our study might be a weakness in showing that training parameters effectively show an influence on race performance. According to O'Toole (22) and Gulbin and Gaffney (7), training distances appear to be a more important factor for competitive success than training paces. This is supported by the findings of Hendy and Boyer (9). According to their investigation, specificity in the relationship between training and performance appears to be supported especially by sports that rely more on the body such as swimming and running, and less on equipment such as cycling. In our male Ironman triathletes, neither average weekly training volume in hours nor in kilometers showed an association with race performance. Furthermore, the lower fitness level of recreational athletes compared when professional athletes might have influenced the findings.

However, in our female Ironman triathletes, training volume was significantly associated with total race time (Figure 5) in contrast to anthropometry (Figure 4). This is in agreement with previous findings in female triathletes. Leake and Carter (16) compared body composition in swimmers, triathletes, and runners. They found no correlates of anthropometry with the prediction of performance, and their conclusion was that training parameters were more important than anthropometric measures in female triathletes. All training variables (training distance in kilometers, training time in hours and training experience in years) were significantly correlated with race performance in their female short-distance triathletes. We must suggest after confirmation of their data that gender-specific differences seem to exist regarding the association of anthropometry and training in triathletes.

This investigation is limited because of the rather small sample size. Regarding the total field of finishers in the race, our male subjects represent 1.2% of all male finishers; our female subjects represent 9.9% out of all female finishers. The distribution of the race time of the male subjects represents the field of male athletes; however, the female subjects represent rather the back of the field. This cross-sectional study is limited regarding the influence and effects of anthropometry and both volume and intensity during training on race performance in Ironman triathletes, because only an intervention trial can answer this question. Other factors that could hypothetically influence performance include nutritional components, matching fluid losses with intake, biomechanics, relative training intensities, both mental and motivational considerations, etc., may also influence performance. These factors were not included in this observational study. Although the limitations are many, in this sample of triathletes, we found the mentioned relationships between anthropometrics and performance. We believe that the observed effect sizes together with the results of the multiple regression models strengthen our conclusion although based on observational findings.

Practical Applications

We found in male nonprofessional Ironman triathletes that anthropometry in contrast to training volume was associated with total race time. In contrast, in female Ironman triathletes, training volume was related to total race time and not to anthropometry. We presume for male Ironman triathletes that anthropometry has a major association with race performance rather than training volume, and that male Ironman triathletes are very close to runners from the anthropometric point of view. The rather small sample size of this study might have influenced the outcome. Because percent body fat and average weekly training volume are not related for both male and female Ironman athletes, presumably the relationship between percent body fat, training volume, and race performance is genetically determined. When percent body fat and training volume show no relationship for both men and women, potential intensity in training could be associated with body fat. This might also explain why male athletes with lower body fat compete faster, and percent body fat is related to split times in swimming, cycling, and running in males, but not in females. However, the rather high BMI of these athletes compared with competitive runners might indicate that these recreational athletes were not training as intensely as professional triathletes do. In future studies, the relationship between anthropometry, training volume, and race performance should be investigated in a larger group of male and female Ironman triathletes, including professional triathletes.